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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 14

k-Degree Anonymity Model for Social Network Data Publishing

MACWAN, K. R. See more information about MACWAN, K. R. on SCOPUS See more information about MACWAN, K. R. on IEEExplore See more information about MACWAN, K. R. on Web of Science, PATEL, S. J. See more information about PATEL, S. J. on SCOPUS See more information about PATEL, S. J. on SCOPUS See more information about PATEL, S. J. on Web of Science
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Download PDF pdficon (1,454 KB) | Citation | Downloads: 15 | Views: 1,945

Author keywords
data privacy, data processing, publishing, social network services, utility programs

References keywords
data(8), social(7), privacy(6), networks(6), preserving(5), network(5), information(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 117 - 124
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04014
Web of Science Accession Number: 000417674300014
SCOPUS ID: 85035757216

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Publicly accessible platform for social networking has gained special attraction because of its easy data sharing. Data generated on such social network is analyzed for various activities like marketing, social psychology, etc. This requires preservation of sensitive attributes before it becomes easily accessible. Simply removing the personal identities of the users before publishing data is not enough to maintain the privacy of the individuals. The structure of the social network data itself reveals much information regarding its users and their connections. To resolve this problem, k-degree anonymous method is adopted. It emphasizes on the modification of the graph to provide at least k number of nodes that contain the same degree. However, this approach is not efficient on a huge amount of social data and the modification of the original data fails to maintain data usefulness. In addition to this, the current anonymization approaches focus on a degree sequence-based graph model which leads to major modification of the graph topological properties. In this paper, we have proposed an improved k-degree anonymity model that retain the social network structural properties and also to provide privacy to the individuals. Utility measurement approach for community based graph model is used to verify the performance of the proposed technique.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 524]

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[8] Ying, Xiaowei, and Xintao Wu. "Randomizing social networks: a spectrum preserving approach." In Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, pp. 739-750, 2008.

[9] L. Backstrom, C. Dwork, and J. M. Kleinberg, "Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography," Commun. ACM, vol. 54, no. 12, pp. 133–141, 2011.
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[16] Y. Wang, L.Xie, B. Zheng, and K. C. Lee, "High utility k-anonymization for social network publishing", Knowledge and Information Systems, vol. 41, no. 3, pp. 697-725, 2014.
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[CrossRef] [SCOPUS Times Cited 99]

References Weight

Web of Science® Citations for all references: 32,558 TCR
SCOPUS® Citations for all references: 32,799 TCR

Web of Science® Average Citations per reference: 1,480 ACR
SCOPUS® Average Citations per reference: 1,491 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-07-21 05:42 in 124 seconds.

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